Inter-area Oscillation Modes Research Articles

Impact of the Exciter and Governor Parameters on Forced Oscillations

In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.

Determination of dominant oscillation section in power system based on MSSA algorithm and dissipated energy

The identification of dominant oscillation paths is one of the key aspects in studying the stability of inter-area power grids. This paper proposes an improved multi-channel singular spectrum analysis (MSSA) algorithm combined with dissipative energy to identify dominant oscillation section in power systems. First, an improved MSSA method is introduced, which determines the order using the singular value energy spectrum and can effectively identify inter-area oscillation modes in power systems. Second, an energy function analysis method is proposed, which is well-suited for determining dominant oscillation section in power systems. This method is minimally affected by time-varying and nonlinear factors and allows for the visualization of generator states. Finally, the IEEE 68-bus system is used to construct an equivalent model, demonstrating the method's versatility. Simulation results validate the noise resistance, computational efficiency, and applicability to topology change scenarios of the proposed method. This method can accurately analyze inter-area oscillation modes and help to establish the dominant oscillation section.

Improving Interarea Mode Oscillation Damping in Multi-Machine Energy Systems through a Coordinated PSS and FACTS Controller Framework

Power system stability is of paramount importance in the context of energy sustainability. The reliable and efficient operation of power systems is crucial for supporting modern societies, economies, and the growing demand for electricity while minimizing environmental impact and increasing sustainability. Due to the insufficient effect of power system stabilizers (PSSs) on damping the inter-area mode oscillations, Flexible AC Transmission System (FACTS) devices are utilized for damping this mode and stabilizing power systems. In the present study, a novel optimization framework considering different and variable weight coefficients based on eigenvalue locations is presented, and the parameters of PSS and variable impedance devices, including static Volt-Ampere Reactive (VAR) compensator (SVC) and Thyristor-Controlled Series Compensator (TCSC) (comprising amplifying gain factor and time constants of phase-compensating blocks), are optimized in a coordinated manner using the proposed optimization framework built based on genetic algorithm (GA). Moreover, in the suggested optimization framework, the locations of FACTS devices and control signals are considered optimization parameters. Numerical results for the IEEE 69-bus power system demonstrated an effective improvement in the damping of inter-area modes utilizing the offered approach.

Tracking and control of power oscillation dampings in transmission lines using PV STATCOM

This paper proposed a new control of PV solar system as a FACTS device STATCOM, called PV-STATCOM, for power oscillation damping (POD) in transmission systems. In this proposed control, as soon as power oscillations due to a system disturbance are detected, the solar farm briefly discontinues its real power generation function and makes its entire inverter capacity available to operate as a STATCOM for POD. As soon as power oscillations are damped, the solar farm restores real power output to its pre-disturbance level while keeping the damping function activated. This results in much faster restoration than that specified in grid codes. During the nighttime, the solar farm performs POD with its entire inverter capacity. The proposed control system provides a significant increase in power transfer capacity on a 24/7 basis in systems that exhibit both local inertial and inter-area oscillatory modes. The developed PV-STATCOM is about 50–100 times cheaper than an equivalent STATCOM for providing POD at the same location. This new control method provides more savings for transmission utilities and opens up a new revenue-making opportunity for solar farms

Two-area power system stability analysis by frequency controller with UPFC synchronization and energy storage systems by optimization approach

AbstractAn optimization approach for two-area power system with Unified Power Flow Controller (UPFC) is proposed in this paper. The proposed method is the Atomic Orbital Search (AOS) approach. The proposed approach is applied to achieve full utilization of UPFC and keeps the parameters uncertain. The multivariable PI controller is utilized to control the system controller and eliminates the negative interaction of the controllers. The proposed approach combines the two subsystems by converting algebraic subsystem using differential approximation, which leads to a nonlinear system. The proposed approach provides efficient voltage regulation and quicker damping of inter-area mode oscillations. The proposed UPFC controller eliminates generator oscillation and fault condition, which guarantee the stability of the system as well as provides dynamic power flow control under the tie-line. At last, the proposed method is simulated on MATLAB platform and compared with existing methods. From this comparison, it is shown that the proposed approach provides less oscillation than the existing approach.

Optimal design and control loop selection for a STATCOM wide-area damping controller considering communication time delays

Wide-area damping controllers (WADC) have been shown to be effective in damping interarea oscillation modes. The varying nature of power system operating point requires the design of an adaptive WADC. Majority of the existing control design techniques rely on empirical approaches at one or more of their design stages, which makes it difficult to implement in adaptive control techniques. Therefore, the purpose of this study is to provide a systematic procedure based entirely on numerical approaches for designing a STATCOM-based WADC. The proposed procedure involving control loop selection, control design and adaptive time delay compensation, can then be implemented in adaptive WADC schemes. Geometric analysis has been used to choose the STATCOM location. Then, a novel optimization-based signal selection methodology is proposed. The proposed methodology employs a combination of multiple input signals to optimally generate a signal with the required modal objectives like enhancing observability and reducing interaction between different control loops. After that, another optimization problem is formulated to tune the controller parameters using integral of time multiplied by absolute error (ITAE) as the optimization objective. Both optimization problems have been evaluated using two optimization techniques: Particle swarm optimization (PSO) and self-adaptive multi-population elitist Jaya algorithm (SAMPE-JAYA). Finally, an adaptive delay compensator is designed to mitigate the effects of communication time delays. The proposed control strategy is applied on the IEEE 68-bus system and the controller performance is evaluated by modal analysis and multiple time-domain simulation scenarios.

Integration analysis of VSG-based IBRs on power system dynamics

Inverter-based resources (IBRs) are key components in future power electronics dominated power systems. Virtual synchronous generator control-based IBRs will take the role of a certain number of synchronous generators (SGs). Although VSG imitates SG using control loops, it still demonstrates different dynamic performances and is worth a careful study. This paper mainly focuses on the impact of VSGs on power system inter-area oscillations. SGs in different locations are either completely or partially substituted with VSGs in Kundur's two-area-four-machine system to examine the influence of VSGs on system oscillatory dynamics. The impact of VSG control parameters on local and inter-area oscillation modes is also investigated. The results show that the damping coefficient of the active power loop Dp is crucial to system stability. The VSG with a properly designed Dp is advised to completely substitute an SG with a high participation factor to provide more damping to both local and inter-area oscillation modes.

Sensitivity analysis of power system small signal stability to geomagnetic disturbances

Geomagnetically induced currents driven by geomagnetic disturbances can cause half-cycle saturation of transformers and consequently affect the power grid with reactive losses. The impact of geomagnetic disturbances focuses on voltage stability, and the research object is the conventional system without considering wind power. In this paper, we study the impact of geomagnetic disturbances on the small signal stability of doubly-fed induction generators integrated systems. After the reactive power loss caused by geomagnetic disturbances and wind power output are equivalent to grounding admittances, the influence of geomagnetic disturbances on small signal stability can be transformed into its impact on the equivalent electrical distance between synchronous generators by shrinking the admittance matrix. Then, the electromagnetic power of synchronous generators is affected, and the impact of wind farm ratio and geomagnetic disturbances is incorporated into the system state equation. The results show that the oscillation frequency and damping ratio increase first and then decrease with the increase of wind power ratio when geomagnetic disturbances occur, but they are generally smaller than those without geomagnetic disturbances. The sensitivity of inter-area oscillation mode to induced geoelectric fields is higher than that of intra-area oscillation modes, and the sensitivity to reactive power losses caused by geomagnetic disturbances in substations is different, which provides a reference for further research on optimizing blocking devices placement to mitigate the impact of geomagnetic disturbances on small signal stability.

Indirect adaptive fuzzy finite time synergetic control for power systems

Introduction. Budget constraints in a world ravenous for electrical power have led utility companies to operate generating stations with full power and sometimes at the limit of stability. In such drastic conditions the occurrence of any contingency or disturbance may lead to a critical situation starting with poorly damped oscillations followed by loss of synchronism and power system instability. In the past decades, the utilization of supplementary excitation control signals for improving power system stability has received much attention. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp low-frequency oscillations caused by load disturbances or short-circuit faults. Problem. Adaptive power system stabilizers have been proposed to adequately deal with a wide range of operating conditions, but they suffer from the major drawback of requiring parameter model identification, state observation and on-line feedback gain computation. Power systems are nonlinear systems, with configurations and parameters that fluctuate with time that which require a fully nonlinear model and an adaptive control scheme for a practical operating environment. A new nonlinear adaptive fuzzy approach based on synergetic control theory which has been developed for nonlinear power system stabilizers to overcome above mentioned problems. Aim. Synergetic control theory has been successfully applied in the design of power system stabilizers is a most promising robust control technique relying on the same principle of invariance found in sliding mode control, but without its chattering drawback. In most of its applications, synergetic control law was designed based on an asymptotic stability analysis and the system trajectories evolve to a specified attractor reaching the equilibrium in an infinite time. In this paper an indirect finite time adaptive fuzzy synergetic power system stabilizer for damping local and inter-area modes of oscillations for power systems is presented. Methodology. The proposed controller design is based on an adaptive fuzzy control combining a synergetic control theory with a finite-time attractor and Lyapunov synthesis. Enhancing existing adaptive fuzzy synergetic power system stabilizer, where fuzzy systems are used to approximate unknown system dynamics and robust synergetic control for only providing asymptotic stability of the closed-loop system, the proposed technique procures finite time convergence property in the derivation of the continuous synergetic control law. Analytical proofs for finite time convergence are presented confirming that the proposed adaptive scheme can guarantee that system signals are bounded and finite time stability obtained. Results. The performance of the proposed stabilizer is evaluated for a single machine infinite bus system and for a multi machine power system under different type of disturbances. Simulation results are compared to those obtained with a conventional adaptive fuzzy synergetic controller.

Data-Driven Interarea Oscillation Analysis for a 100% IBR-Penetrated Power Grid

In this research, a 100% inverter-based resource (IBR)-penetrated bulk power system (BPS) is examined for possible interarea oscillations. A testbed of a BPS constructed in PSCAD, with both grid-forming inverter (GFM)-based IBRs and grid-following inverter (GFL)-based IBRs, is utilized for simulation. For linear analysis, a data-driven method is used to obtain the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dq$ </tex-math></inline-formula> admittance models of the IBRs. This information, combined with our knowledge of the network admittance, leads to eigenvalue analysis of the power grid. Furthermore, for the dominant 3-Hz mode, extended frequency-domain mode shape analysis, along with subgroup effect analysis, and network decomposition analysis are conducted. The analysis results can precisely tell the influencing factors of the oscillation mode. This mode is identified as an interarea oscillation mode. It has a different characteristics compared to the traditional electromechanical interarea mode. Specifically, the network decomposition analysis provides new insights on interarea oscillation modes. The analysis results are all confirmed by the electromagnetic transient (EMT) simulation results.

Intelligent Design of Multi-Machine Power System Stabilizers (PSSs) Using Improved Particle Swarm Optimization

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

Data gaps and degraded space-time resolution for modal decomposition: A compensator approach

High-resolution empirical mapping methods of dynamic oscillation responses for large power systems have recently been considered to treat the data gaps and its space-time compensation. Although virtually-designed data of signals are not a real condition for monitoring systems, these can be implemented to study objectively the moving features and dominant paths of inter-area oscillation modes. This paper demonstrates the application of the space-time interpolation of wide-area measurements to compensate data gaps and degraded space-time resolution for modal decomposition. Contributions of empirical mode coherence for splitting and clustering data in density factors are also presented to the classification area. The method involves the space-time high-resolution interpolation of empirical mode components for splitting closely-related oscillation modes at intrinsic timescales, preserving their multivariate waveform features. Therefore, high-resolution empirical viewing maps of remotely-sensed dynamic responses, with degraded space-time resolutions, are derived to study the observance and dominant paths of wide-area oscillation modes. The proposed approach is numerically applied to the IEEE 16-generator 68-bus test system for efficiently defining empirical viewing maps of inter-area oscillation modes.

Design of Velocity PID-Fuzzy Power System Stabilizer Using Particle Swarm Optimization

Power system stabilizer (PSS) is used to damping power system local and interarea modes of oscillation. It will act as additional signal through the generator excitation system which produces a component of electrical torque in phase with speed variation. Fuzzy controller is nonlinear. It is more difficult to set the fuzzy controller gains compared to conventional PID controller. This research proposed velocity (PID-FPSS) its gains are tuned off-line by particle swarm optimization techniques (PSO). The objectives are to damp local-area mode of oscillations that occur following power system disturbances. The effectiveness of the proposed technique is illustrated by applying the velocity PID-FPSS to a single-machine infinite bus power system that is typically used in the literature to test the performance of power system stabilizers. The simulation studies has been tested in MATLAB SIMULINK package . A comparison between the proposed PID-FPSS and a well-tuned and designed robust power system stabilizer (RPSS) confirms the superiority of the PID-FPSS.

Genetic-TLBO Algorithm for Power System Stabilizer

Background: A large number of nature-based optimization methods have been proposed to use as efficient tools in scientific studies. Genetic Algorithm (GA), which operates based on human genetical evolution, has been an outstanding mostly used solver in a wide range of applications. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Initialization, selection, crossover, and mutation are the main parts of the GA population-based method which enables GA to have a prominent explorative feature. On the other hand, the Teaching Learning Based Optimization algorithm (TLBO) is of great performance during searching for the optimum solution among individuals. Therefore, it is expected that the combination of both algorithms in a certain logical way improves the optimization time. Objective: The study intends to determine ways of improving performance of the TLBO algorithm to solve a complex non-linear problem. Power system studies are one of the most complex problems for analysis. Therefore, a powerful heuristic optimization procedure would have a valuable contribution in solving such problems. In addition, the proposed heuristic algorithm will help scientists to apply the technique to their problems. Materials and Methods: According to the aforementioned explanation, a new efficient optimization approach is proposed which optimizes the parameters of multi-machine power system stabilizers (PSSs). The TLBO algorithm includes two different stages in its main structure, which are aptly called teacher and student stages. The student stage of TLBO is replaced by the genetic algorithm in order to improve the explorative feature of the main TLBO. The PSS parameters are obtained for four PSSs which are connected to four generators. Results: The performance of the proposed stabilizer is compared with other formerly designed stabilizers reported in the literature consisting of multi-band PSSs for two areas four-machine power system. Simulation results demonstrate the effectiveness and robustness of the proposed PSS in damping local and inter-area oscillation modes under various disturbances and confirm its superiority in comparison with the other types of PSSs. Conclusion: : A search heuristic method like the genetic algorithm can dramatically improve the performance of meta-heuristic optimization technique. In actuality, the TLBO as a meta-heuristic optimization technique suffers from a direct search of random solutions in its primary stage. Then, the TLBO relinquishes some parts of search space which may restrict the algorithm to find absolute maximums or minimums. In this condition, the GA with a great ability in searching the whole search space effectively improves the TLBO. According to the obtained results, the proposed algorithm, named Genetic-TLBO, obviates the conventional TLBO flaws successfully.

Performance Analysis of Data-Driven Techniques for Detection and Identification of Low Frequency Oscillations in Multimachine Power System

In power systems, identification and damping of low-frequency oscillations(LFO) is very crucial to maintain the small signal stability. Hence the computation of eigenvalues, eigenmode shapes, participation factors, and coherency of generators are essential parameters of critical LFO modes. The existing data-driven approaches explore either one or two aspects of modal parameters from the dynamic pattern of the measurement data. In the present work, two approaches i) Iterative Approach(IA) ii) Non-Iterative Subspace(SS) method of data-driven techniques are used to estimate the state-space model of the system under study from the measurement data in a holistic framework. Based on the estimated system model, the eigenvalues of LFO, eigenmode shapes, participation factor, and coherency of associated generators participating in electromechanical oscillations are computed. Finally, from the estimated participation factors for the Inter-area oscillation mode (IAM), the Static Synchronous Compensator (STATCOM) damping controller is designed and placed at the generator with the highest participation factor for damping of inter-area oscillation. The enhancement of damping ratio of inter area mode with STATCOM damping controller is estimated and verified using IA & SS data driven approaches for the first time. In this work, IA uses prediction-error minimization algorithm (PEM) & Parallel computing techniques and SS method uses Multivariable Output Error State Space (MOESP) algorithm for the estimation of Hankel matrix from the measured data. The effectiveness of data-driven approaches are demonstrated by the simulation of a IEEE 4-machine,10-bus power system using MATLAB / Simulink. IA & SS methods incorporating wavelet based denoising techniques are very effective in identifying the LFO modes even with noisy measurement. The efficacy of the denoising to suppress the effect of noise is demonstrated by comparing with noiseless environment. The results of data-driven approaches indicate their high degree of accuracy and efficiency in being consistent with Eigenvalue analysis (EA) performed on the system.

Application of partial eigenvalue assignment techniques to dampen electromechanical oscillations in multi-machine power systems

Abstract In this study, a methodology for partial eigenstructure assignment (PEVA) is applied to dampen electromechanical oscillations in electrical multi-machine power systems. The approach is anchored in allocating a small number of undesirable eigenvalues, for example, which are poorly damped, preserving the other eigenvalues in the system - the so-called no-spillover spectrum. The new position of the selected eigenvalues is carried out based on the partial controllability analysis of the system, in order to minimize the control effort. Simulation examples using a system with 68 buses, 16 generators and five areas showed that the presented methodology is efficient in dampening the local and inter-area oscillation modes when compared to the classic power system stabilizers (PSS). The quality of the solution is illustrated through computer simulations, eigenvalues tables and mode-shapes.

A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability

This paper proposes a novel unified prediction approach for both small-signal and transient rotor angle stability as opposed to other studies that have only addressed transient rotor angle stability. Deep learning techniques are employed in this paper to train an online prediction model for rotor angle stability (RAS) using the voltage phasor measurements which are collected across the entire system. As a result, the trained model provides a fast yet accurate prediction of the transient stability status when a power system is subjected to a disturbance. Also, if the system is transiently stable, the prediction model updates the power system operator concerning the damping of low-frequency local and inter-area modes of oscillations. Therefore, the presented approach provides information concerning the transient stability and oscillatory dynamic response of the system such that proper control actions are taken. To achieve these objectives, advanced deep learning techniques are employed to train the online prediction model using a dataset which is generated through extensive time domain simulations for wide range of operating conditions. A convolutional neural network (CNN) transient stability classifier is trained to operate on the transient response of the phasor voltages across the entire system and provide a binary stability label. In tandem, a long-short term memory (LSTM) network is trained to learn the oscillatory response of a predicted stable system to capture the step-by-step dynamic evolution of the critical poorly damped low-frequency oscillations. The superior performance of the proposed model is tested using the New-England 10-machine, 39-bus, IEEE 16-machine, 68-bus, 5-area and IEEE 50-machine, 145-bus test systems and is verified with time domain simulation.

Markov Game for Securing Wide-Area Damping Control Against False Data Injection Attacks

While wide-area communication networks facilitate the exchange of measurements and control signals for wide-area damping controllers, they bring in new threats of cyberattacks. This article formulates the dynamic interaction between an intelligent spoof attacker and the wide-area damping control (WADC) center as a zero-sum two-player Markov game. In this Markov game, the attacker intelligently spoofs the communication links sending wide-area control signals to selected generators while the WADC selectively encrypts transmitted control signals with limited cyberresources. The successful spoof of specific communication links leads to the degradation of the WADC performance and even to system instability indicated by the real part of the targeted interarea oscillation mode. The potential performance degradation in the physical layer is uniquely included in the cost function of this game while the other two parts of the cost function are the cybersecurity investments of both the attacker and the WADC (defender). The minimax Q-learning approach is used to obtain the optimal defense and attack strategies. Simulation results show that the strategies for the attacker and the WADC converge within 3000 iterations by using the minimax Q-learning approach. The proposed Markov game provides a cyberphysical framework to make the optimal defense strategy for the WADC.

A hybrid modified grey wolf optimization‐sine cosine algorithm‐based power system stabilizer parameter tuning in a multimachine power system

SummaryDamping of low‐frequency oscillations due to the unpredictable perturbations of a power network has always been a challenging task. In an interconnected power network, power system stabilizers (PSSs) are in practice to damp out these low‐frequency oscillations by providing a necessary control signal to the automatic voltage regulator unit based on the deviation in generator speed/power output. This article proposes a novel approach of hybrid modified grey wolf optimization‐sine cosine algorithm for tuning the parameters of PSS of an interconnected multimachine power system. The optimal parameter tuning of PSS with the proposed algorithm is achieved by considering a multiobjective function comprises of improving the damping and eigenvalue characteristics of the consolidated multimachine system. A benchmark model of two area four machine system is adopted to investigate the performance achieved with the proposed algorithm in the simultaneous damping of the local and interarea mode of oscillations in a multimachine power system. The system study has been carried out under a self‐clearing fault condition, and the detailed analysis is presented by analyzing the eigenvalues, and their corresponding natural frequencies, damping ratios. The damping nature achieved for the system states under system uncertainties with the proposed algorithm is also presented. The performance obtained from the proposed hybrid algorithm has been compared with the standalone and state‐of‐the‐art optimization methods.

Designing of wide-area damping controller for stability improvement in a large-scale power system in presence of wind farms and SMES compensator

The main aim of this paper is the stability improvement of a large-scale power system including wind farms and SMES (Superconducting Magnetic Energy Storage) compensator. For this purpose, at first, the mathematical model of the whole system is extracted in order to accurately investigate the role of wind farms and SMES in the stability studies. The stability analysis is based on the design of damping controllers which is provided through the global signals dependent on the time delay and local signals without the time delay. In modeling of the time delay, damping factor of inter-area oscillation mode is used to obtain larger delay margin, and then, the Lyapunov-Krasovskii function and inequality integral are employed to evaluate the stability condition related to the time delay. The controllability/observability index is used to choose the best candidate signals in the input of WADC (Wide-Area Damping Controller) in order to enhance the residual value of the weakest critical mode identified by modal analysis. The simulation results are investigated through different scenarios of wind turbine and power system in non-linear time domain as well as frequency domain using eigenvalues analysis. The results verify the effectiveness of the proposed controller under various time delays.